Towards a new real-time irrigation scheduling method: observation, modelling and their integration by data assimilation

Irrigated agriculture is very important in securing food production for an increasing population over the next decades. Given the scarcity of water resources, optimal irrigation management is needed to reduce water usage while maintaining maximal crop productivity. The irrigation scheduling methods are normally based on soil water content (SWC) measurements, e.g. from soil moisture sensors. Meanwhile land surface models such as the Community Land Model (CLM) have been commonly used to simulate SWC, crop status and hydrological processes. Data assimilation (DA) can combine different measurement data with a numerical model to get optimal estimation of model states. The integration of SWC measurements and Community Land Model using a sequential data assimilation method is promising to improve the real-time prediction of SWC and the calculation of irrigation demand. One of the aim of this PhD work is to introduce a new CLM-DA based real-time irrigation scheduling method and test it in a real world case, to explore the possibility of using other sources of SWC measurements (e.g. Cosmic-ray Neutron Sensing) for irrigation ,scheduling and to conduct the uncertainty analysis of irrigation modelling with CLM